Triple

T14413358
Position Surface form Disambiguated ID Type / Status
Subject 5 Days of War E357385 entity
Predicate producer P490 FINISHED
Object Michele Weisler
Michele Weisler is a film producer known for her work on the war drama "5 Days of War."
E1171046 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Michele Weisler | Statement: [5 Days of War, producer, Michele Weisler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michele Weisler
Context triple: [5 Days of War, producer, Michele Weisler]
  • A. Hal Bidlack
    Hal Bidlack is an American political science professor, retired U.S. Air Force officer, and public speaker known for his work in skepticism and secular humanism.
  • B. George Weidler
    George Weidler was an American saxophonist and band musician best known for his brief early marriage to singer and actress Doris Day.
  • C. Andrew Weisblum
    Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
  • D. Terry Weissman
    Terry Weissman is a software engineer best known for creating the initial version of the Bugzilla bug-tracking system at Netscape.
  • E. Mel Winkler
    Mel Winkler was an American character actor best known for his distinctive voice work in video games and animation, as well as supporting roles in film and television.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Michele Weisler
Triple: [5 Days of War, producer, Michele Weisler]
Generated description
Michele Weisler is a film producer known for her work on the war drama "5 Days of War."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Michele Weisler
Target entity description: Michele Weisler is a film producer known for her work on the war drama "5 Days of War."
  • A. Hal Bidlack
    Hal Bidlack is an American political science professor, retired U.S. Air Force officer, and public speaker known for his work in skepticism and secular humanism.
  • B. George Weidler
    George Weidler was an American saxophonist and band musician best known for his brief early marriage to singer and actress Doris Day.
  • C. Andrew Weisblum
    Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
  • D. Terry Weissman
    Terry Weissman is a software engineer best known for creating the initial version of the Bugzilla bug-tracking system at Netscape.
  • E. Mel Winkler
    Mel Winkler was an American character actor best known for his distinctive voice work in video games and animation, as well as supporting roles in film and television.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d82793421c8190861eb0e673b085de completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de90cb3c708190822f5506ebf7ee9d completed April 14, 2026, 7:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff6ebeb5ec8190afef40d87c74a8a0 completed May 9, 2026, 5:28 p.m.
NEDg Description generation batch_69ff702588908190a1b1dd1fd6a972f9 completed May 9, 2026, 5:34 p.m.
NED2 Entity disambiguation (via description) batch_69ff70f97eec8190a1f5affdad31f2b2 completed May 9, 2026, 5:38 p.m.
Created at: April 10, 2026, 1:17 a.m.